UAV inspection method for power line based on human visual system

ABSTRACT

This invention discloses a UAV inspection method for power line based on human visual system. Image preprocessing module preprocesses the power line image of the input system. Power line detection module uses human visual attention mechanism to complete segmentation of the power line in the image. Binocular image registration module uses SURF algorithm to provide exact match of the feature points. The obstacle detection and early warning module uses binocular visual principle to calculate the three-dimensional coordinates of the matching point and the power line. The result output and feedback module calculates the vertical distance from the matching point to the power line according to the information about the space coordinates to complete feedback of the information about the obstacle with a threat to the power line. The method can accurately analyze the obstacle of power line in a quantitative manner, and the analysis result is stable and objective.

FIELD

This invention relates to an UAV inspection method for power line basedon human visual system, which belongs to the field of digital imageprocessing and power line automatic inspection.

BACKGROUND

With the vigorous development of China's economy, there are more andmore demands for electric energy. In order to meet the increasing demandof electricity in China, measures shall be taken to continue to expandin the direction of power line, high voltage and large capacity. On theone hand, with the construction of a large number of power lines, thecoverage becomes much broader, terrain conditions are more complex anddiverse, and it is difficult to figure out how to solve the problem ofproviding cross-terrain line maintenance. On the other hand, theenvironment of the power lines also constantly changes with the regionand time. The whole power line system is complex, and the hidden dangerin some key link may affect the users' power supply and system's powersupply safety, resulting in immeasurable economic losses and threateningthe safety of life and property, so power line inspection shall bedeemed as an important part of power system equipment maintenance.

High-voltage power line, that is, overhead line, refers to the powerline which erects the wires on the pole and tower with insulators andpower fittings, and is an important component of the power grid andpower system, which is vulnerable to external impacts and damages. Atpresent, the overhead line accidents mainly include accidents due toexternal damages, wind accidents, lightning accidents and accidentscaused by aging of equipment. The so-called accidents due to externaldamages mainly refer to accidents caused by foreign objects enteringnon-safe areas or distance, such as forest trees, housing constructionand power lines in other non-safe areas, which not only poses a threatto the safety of power lines, but also easily leads to electric shock inthe obstacle itself, fire hazard and other accidents. According torelevant reports, accidents due to external damage account for aboutone-fourth of the total number of trips of the national power lines,causing enormous harm and economic loss to the society.

The traditional power line inspection mode is usually manual inspection,which requires large consumption of manpower resources, but under thecircumstance with large coverage of power lines and diverse needs forthe environment, manual power line inspection has low efficiency andpoor timeliness, as a result of which it often cannot meet therequirements for coverage and timeliness of power line inspection. Inaddition, the traditional manual power line inspection mode is oftenbased on human eye observation and determines according to the staffexperience in the status of the power line. Given its excessivedependence on the staff's status and experience, it cannot give aquantitative analysis of the distance between the obstacle and the powerline and tends to cause false and missed inspection, so it cannot meetthe accuracy requirement for power line inspection.

SUMMARY

The technical problem to be solved by this invention is to provide anUAV inspection system for the power line with regard to the technologygap in the field of accident inspection due to external force in thecurrent power lines, so as to improve the way of power line inspectionand improve the inspection efficiency and accuracy.

To solve the technical problem, this invention provides an UAVinspection method for power line based on human visual system, whichincludes the following steps:

Step (1) for UAV vide capture, using UAV armed with binocular visualmeasuring equipment to obtain video images of distribution of the powerline and environmental information on the power line;

Step (2) by an image preprocessing module, grabbing frames of a sequenceof inputted vide images of the power line, and preprocessing currentframe of the image, including image graying processing and DoG edgedetection, in which, the DoG edge detection uses DoG results ofdifferent parameters to describe the image edge and applies the sameprocessing to left visual image and right visual image in order;

Step (3) by a power line detection module, processing the preprocessedimage based on mathematical morphology, selecting structural factors inthe same direction as the power line to perform repeated dilation andcorrosion processing on the image, removing image noise, and using humanvisual connected domain attention mechanism to select the largest linearconnected domain so as to complete segmentation of the power line in theimage;

respectively recording horizontal coordinate positions (x_(dz1),x_(dz2). . . x_(dzj)) and (x_(dy1),x_(dy2) . . . x_(dyj)) of the power line inleft visual image and right visual image, where x_(dz1),x_(dz2) . . .x_(dzj) and x_(dy1),x_(dy2) . . . x_(dyj) refer to the horizontalcoordinate of a central point of a connected domain of j power lines inthe left visual image and right visual image;

Step (4) by a binocular image registration module, providingregistration for the left visual image and right visual imageseparately, using SURF algorithm to find feature points of the left andright visual edge images which have been preprocessed, obtainingdescriptor of a current feature point, performing exact matching for thefeature points and recording location information of the exact matchingpoint in left and right visual images (x_(z1),y_(z1))˜(x_(y1),y_(y1)),(x_(z2),y_(z2))˜(x_(y2),y_(y2)) . . . (x_(zn),y_(zn))˜(x_(yn),y_(yn)),of which n refers to the number of all feature points included in singleimage x_(zn),y_(zn) and x_(yn),y_(yn) refer to position coordinates ofcorresponding features points in the left and right visual images,respectively;

Step (5) by an obstacle detection and early warning module, based on thehorizontal coordination position of the power line in left and rightvisual images (x_(dz1),x_(dz2) . . . x_(dzj)), (x_(dy1),x_(dy2) . . .x_(dyj)) and locations of the exact matching points in left and rightvisual images (x_(z1),y_(z1))˜(x_(y1),y_(y1)),(x_(z2),y_(z2))˜(x_(y2),y_(y2)) . . . (x_(zn),y_(zn))˜(x_(yn),y_(yn)),using binocular visual principle to calculate three-dimensional spacecoordinates of the matching points and the power line, and calculating avertical distance from the matching points to the power line accordingto the space coordinate information;

Step (6) by a result output and feedback module, feeding back obstacleinformation of an obstacle that threatens the power line, when the spacevertical distance between the matching point and the power line ishigher than a predetermined threshold, feeding back detailed informationabout the obstacle to a computer software interface and recordingobstacle detection time and geographical location; when the spacevertical distance between the matching point and the power line is lowerthan the predetermined threshold, not processing the current matchingpoint; applying the same processing to all matching points in thecurrent frame image, feeding back the obstacle information of allobtained frame images in order, and completing record of the obstacleinformation in an inspection process.

The beneficial effect achieved in this invention is that it uses thebinocular visual technology to detect and analyze the external obstaclesof the power line and it can provide a massive, rapid information searchof the power lines mainly through the joint inspection of UAVs armedwith binocular visual measuring equipment to obtain the imageinformation about the power lines and equipment in real time and enablethe expert system to complete the quantitative analysis of the faultsand hidden dangers of the power lines. Compared to traditional manualinspection, UAV inspection is not bound by terrain, environment, stateand other factors, and it can be used to monitor the distribution ofpower lines and the surroundings in real time and can use the binocularvisual technology of human eye to provide a quantitative analysis andearly warning of the power line distribution, fault and other problems,which is characterized by high inspection efficiency, versatility andgood timeliness.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is the Modular Structure Diagram of the UAV Inspection System forPower Line of the Invention;

FIG. 2 is the Physical Model Diagram of the UAV Inspection System forPower Line of this Invention;

FIG. 3 is the Process Flow Chart of the UAV Inspection System for PowerLine of this Invention;

FIG. 4 is the Dimension Diagram of the Filter in the Scale Space;

FIG. 5 is the Schematic Diagram of Binocular Vision Imaging;

FIG. 6 is the Diagram of Matching of Features Points in the Left andRight Visual Images;

FIG. 7 is the Diagram of Annotations of Accident Points Due to ExternalForce in the Left Visual Image.

DETAILED DESCRIPTION

The UAV inspection method for power line based on human visual system ofthis invention includes the hardware work system and software operatingsystem, and the modules of the entire system are shown in FIG. 1, ofwhich the software system includes the UAV video information capturemodule, while software system includes image preprocessing module, powerline detection module, binocular image registration module, obstacledetection and early warning module and result output and feedbackmodule.

FIG. 2 shows the Physical Model Diagram, which first uses the UAV armedwith binocular visual measuring equipment to acquire information aboutthe power lines to be inspected and the surroundings and control theflight of the UAV, and the flight direction of UAV is in parallel withthe distribution direction of power lines. It transmits the obtainedbinocular video sequence to the software operating system, which willextract the image frame at a certain interval from the video sequence toprovide all image frames with image preprocessing, power line detection,left and right visual image registration, obstacle detection and earlywarning and result output and feedback in sequence to complete recordand feedback of obstacle information in the process of UAV inspection.

With regard to the UAV inspection method for power line based on humanvisual system of this invention, the specific process is shown in FIG.3:

(1) When it is required to detect the power line in some region, be sureto first use the UAV armed with binocular visual measuring equipment toinspect the power lines and the surroundings for acquisition of relevantinformation, of which the binocular visual measuring equipment consistsof two cameras with same specifications, the camera pixel is not lowerthan 500 W, the parallax range b between two cameras for fixation is 120mm and the focal length of the camera f is known as 400 mm≤f≤100 mm, andthe two cameras are used to shoot in parallel in the same direction. TheUAV is controlled by the staff, whose uniform flight direction is inparallel with the distribution direction of power lines, and it isuniform and stable in the flight. The binocular visual hardware systembased on dual cameras shall be installed on a stable platform of theUAV, which will save the obtained left video sequence l_(z) and rightvideo sequence l_(y) to the storage device to the UAV, and uses mobilewireless network to transmit to the software system.

(2) In the process of UAV inspection, it will transmit the obtained leftvideo sequence l_(z) and right video sequence l_(y) from the UAV to thesoftware system in real time, and the software system will first backupthe data obtained in the data segment and the staff will enter theshooting time and location. With regard to processing of the left videosequence l_(z) and right video sequence l_(y), be sure to firstcontinuously grab the frame separately from the left video sequencel_(z) and right video sequence l_(y) at a certain interval, whichrequires that all frames of images can be pieced together to recover thecomplete inspection image information and there is no information losswhen adjacent frames are pieced together, so as to form the left imagesequence T_(z) and right image sequence T_(y), of which both the leftimage sequence T_(z) and right image sequence T_(y) contain m frames ofimages, separately are T_(z1),T_(z2) . . . T_(zm) and T_(y1),T_(y2) . .. T_(ym). With regard to preprocessing of corresponding left and rightvisual images, be sure to first process the first left visual imageT_(z1) and the first right visual image T_(y1), and then process thecorresponding left and right visual images in sequence until the mthleft visual image T_(zm) and the mth right visual image T_(ym) areprocessed.

Preprocessing the first left visual image T_(z1) and the first rightvisual image T_(y1) includes image grayscale processing and DoG(Difference of Gaussian) edge detection.

Grayscale processing of the said image turns color image into thesingle-channel grayscale image H_(z1) of the first left visual image andthe single-channel grayscale image of the first right visual imageH_(y1).

In the said DoG edge detection, Difference of Gaussian (DoG) is thedifference of Gaussian function, which obtains the LPF results of aframe of image through convolution of the image of Gaussian function.Same as the Gaussian in the Gaussian low pass filter, the said Gaussianis a function, that is, normal distribution function. Difference ofGaussian, namely, difference of Gaussian function, is the difference oftwo Gaussian images. When it is specific to image processing, that is,subtract the results of Gaussian filter of the same frame of image underdifferent parameters to obtain DoG image, that is, the edge detectionresult. In addition, DoG operation is defined as:

$\begin{matrix}{D = {{\frac{1}{2\pi}\left\lbrack {{\frac{1}{\sigma_{1}^{2}}e^{- \frac{({x^{2} + y^{2}})}{2\sigma_{1}^{2}}}} - {\frac{1}{\sigma_{2}^{2}}e^{- \frac{({x^{2} + y^{2}})}{2\sigma_{2}^{2}}}}} \right\rbrack}*H}} \\{= {\left\lbrack {{G\left( {x,y,\sigma_{1}} \right)} - {G\left( {x,y,\sigma_{2}} \right)}} \right\rbrack*H}}\end{matrix}$

Where, D refers to the image result after processing, the first constantσ₁=0.6 and the second constant σ₂=0.9, x and y separately refer to thehorizontal and vertical coordinates of the current pixel point in theimage, the window size of the Gaussian filter function is 5×5, G(x,y,σ₁)and G(x,y,σ₂) separately refer to the Gaussian filter function ofdifferent parameters, H refers to grayscale image, “*” stands for theprocessing of the treatment of moving filter toward the whole frame ofimage, and the preprocessed images separately are the left visual edgeimage D_(z1) and the right visual edge image D_(y1).

(3) With regard to segmentation of the power line in the left visualedge image D_(z1) and the right visual edge image D_(y1), first use theloop erosion and dilation operation in mathematical morphology toprocess the left visual edge image D_(z1) and the right visual edgeimage D_(y1); the video capture direction is in parallel with thedistribution direction of power lines, and the direction of power linein the image is in parallel wit the direction of 90° of the image. Imageprocessing in morphology is to move a structural element in the image,and then apply intersection, union and other set operations to the leftvisual edge image D_(z1) and the right visual edge image D_(y1) with thestructural element, of which structural element is the most importantand fundamental concept, where B(x) stands for the structural element,and the erosion and dilation operation of each point A(x,y) in the workspace E is defined as:X=E⊗B={A(x,y):B(x)⊂E}  Erosion:Y=E⊕B={A(x,y):B(y)∩E≠Φ}  Dilation:

Where, ⊂ stands for “included in”, ∩ stands for “intersection operation”and Φ stands for null set. If the structural element B(x) is used toapply erosion to the work space E, the result is the set consisting ofall points of the structural element B(x) included in the work space Eafter translation of the structural element B(x). If the structuralelement B(x) is used to apply dilation to the work space E, the resultis the set consisting of all non-null set points of an intersection setof the structural element B and the work space E after translation ofthe structural element B. Select the linear structure factor with thelength of 3 pixel points and the angle of 90°, apply erosion anddilation operation to the left visual edge image D_(z1) and the rightvisual edge image D_(y1), during which one loop operation includes twoerosion operations and one dilation operation, which lasts for 20 times.

With regard to the image which has completed the loop erosion anddilation operation, measure the area and length of the area of theconnected domain in the image, retain the line segment whose linearshape and area of the connected domain meet the threshold requirements,that is, the power line, remove the noise in the image, completesegmentation of the power line in the image and separately record thehorizontal coordinate positions of (x_(dz1),x_(dz2) . . . x_(dzj)) and(x_(dz1),x_(dz2) . . . x_(dzj)) of the power line in the left visualedge image D_(z1) and the right visual edge image D_(y1), where the leftvisual edge image D_(z1) and the right visual edge image D_(y1)separately contain n power lines and record j horizontal coordinatepositions.

(4) SURF algorithm is used to detect the feature points of the leftvisual edge image D_(z1) and the right visual edge image D_(y1). To makethe registration feature have scale invariance, key points of the imageshall be detected in the scale space. In addition, SURF algorithm is tofilter boxes of different sizes on the original image to form the imagepyramid of different scales.

(41) Use the rapid Hessian detector to extract the feature points, andHessian matrix has good computing time and precision performance. Withregard to certain point, the Hessian matrix under scale can be definedas shown in the formula below, while with regard to certain point (x,y)in the left visual edge image D_(z1) and the right visual image D_(y1),when the scale is σ, the Hessian matrix at the point shall be expressedas:

${H\left( {x,\sigma} \right)} = \begin{bmatrix}{L_{xx}\left( {x,\sigma} \right)} & {L_{xy}\left( {x,\sigma} \right)} \\{L_{xy}\left( {x,\sigma} \right)} & {L_{yy}\left( {x,\sigma} \right)}\end{bmatrix}$

Where, the function L_(xx)(x,σ) refers to the second partial derivativeof the Gaussian function on X-coordinate and the convolution of the leftvisual edge image D_(z1) and the right visual edge image D_(y1) and thepoint (x,y), and the formula is shown below:

${L_{xx}\left( {x,\sigma} \right)} = {D*\frac{\partial^{2}}{\partial x^{2}}{g(\sigma)}}$

Description of L_(xy)(x,σ) and L_(yy)(x,σ) is separately shown in theformula below:

${L_{xy}\left( {x,\sigma} \right)} = {D*\frac{\partial^{2}}{{\partial x}{\partial y}}{g(\sigma)}}$

${{L_{yy}\left( {x,\sigma} \right)} = {D*\frac{\partial^{2}}{\partial y^{2}}{g(\sigma)}}},$

∂ refers to the operation to obtain the partial derivative, Where, thescale space of SURF algorithm is divided by group, images in each groupare obtained after convolution of filters of different sizes, and thefilter size will gradually increase. Assuming that the size of N×N, andthe corresponding size is σ=1.2×N/9. In addition, the sizes of filtersin different groups in the scale space are shown in FIG. 4, wherehorizontal coordinate stands for the changes in filter size whilevertical coordinate stands for different groups.

To make the algorithm have the feature of invariant direction, SURFalgorithm will re-specify the only direction for each interest pointaccording to the information about the pixel point around the featurepoints, and the specific steps are as follows:

a) Take feature points as center to calculate the horizontal andvertical responses of Harr wavelet in the circular field with a radiusof 6σ, of which the sampling step length is σ and the wavelet size is4σ;

b) Take feature points as center to apply Gaussian weighting (2σ) towavelet response, so that the weight value close to the feature point islarge and that away from the feature point is small, and the newhorizontal and vertical responses are obtained;

c) Finally, use a fan-shaped window with an angle of 60° to traverse theentire circle until the total response within the fan-shaped window isthe strongest, at this time the direction within the fan-shaped windowis the principal direction of the interest point.

(42) Set the feature point as the central point and construct a box withthe size of 20σ along the principal direction of the central point, thendivide the region into 16 small regions, calculate the wavelet responsewithin each subregion (5σ×5σ) and obtain the direction of 0° and thevector Σdx, direction of 90° and the vector Σdy, direction of 180° andthe vector Σd|x| and direction of 180° and the vector Σd|y|, and thenconstruct the 4D feature vector v=(Σdx,Σ|dx|,Σdy,Σ|dy|) to express eachsubregion, and finally form the 64D descriptor of the point.

Assuming that the size of the left visual edge image D_(z1) and theright visual edge image D_(y1) are both M×N, and the images are placedhorizontally at the same coordinate axis to form the image with the sizeof M×2N, and the matching of feature points in the left edge image andthe right visual edge image are shown in FIG. 6. In addition, the set offeature points of the left visual edge image D_(z1) and the right visualedge image D_(y1) detected by the SURF method can be expressed as:Pos1={(x′ ₁ ,y′ ₁),(x′ ₂ ,y′ ₂), . . . ,(x′ _(p) ,y _(p))}Pos2={(x ₁ ,y ₁),(x ₂ ,y ₂), . . . ,(x _(q) ,y _(q))},

Where, p and q separately refer to the number of feature points of theleft visual edge image D_(z1) and the right visual edge image D_(y1);according to the prior knowledge about the consistency of slopedirection between the final correct matching points, the steps of thematching methods of the feature points of this invention are as follows:

a) With regard to each point i in the feature point set Pos1 of the leftvisual edge image, calculate the Euclidean distance of it from allpoints in the feature point set Pos2 of the right visual edge image, andselect the corresponding point of the minimum Euclidean distance as therough matching Point i;

b) Calculate the Euclidean distance of all rough matching point pair,sort the matching point pairs by the Euclidean distance in ascendingorder and delete the point pair with multiple points towards one point,at this time the feature points in the left visual edge image D_(z1) andthe right visual edge image D_(y1) can be separately expressed as thefeature point set Pos1′ of the fixed (corrected) left visual edge imageand the feature point set Pos2′ of the fixed right visual edge image;

c) Select the first K₁ pairs of matching points in the feature point setPos1′ of the fixed left visual edge image and the feature point setPos2′ of the fixed right visual edge image to express asPos_K₁={{(x′₁,y′₁),(x₁,y₁)},{(x′₂,y′₂),(x₂,y₂)}, . . . ,{(x′_(n),y′_(n)),(x_(n),y_(n))}}, which is called Set1;

Select the first K₂ pairs of matching points in the feature point setPos1′ of the fixed left visual edge image and the feature point setPos2′ of the fixed right visual edge image to express as Pos_K₂, where

Pos_K₂={{(x′₁,y′₁),(x₁,y₁)},{(x′₂,y′₂),(x₂,y₂)}, . . . , {(x′_(K) ₂,y′_(K) ₂ ),(x_(K) ₂ ,y_(K) ₂ )}}, is called as Set 2,

where K₁<K₂;

d) With regard to all the point pairs in Set 2, calculate the slopebetween two points as shown in the formula below, and round it to formSlope Set k: k={k₁,k₂, . . . ,k_(w)},

${k_{w} = \frac{y_{w}}{x_{w} - x_{w}^{\prime}}},{{1 \leq w \leq K_{2}};}$

e) Calculate the frequency of each slope in Slope Set k, screen theslope with the frequency of greater than and equal to 2 to form a newset k_new={k₁,k₂, . . . ,k_(t)}, where t refers to the total quantity offeature points obtained; if the frequency of each slop in Slope Set k is1, select the slope of the first 2K₂/3 pairs of points to form a new setk_new;

f) Traverse and calculate the slope of all point pairs in the featurepoint set Pos1′ of the fixed left visual edge image and the featurepoint set Pos2′ of the fixed right visual edge image, and screen all thepoint pairs with the slope falling between the interval of[k_(t)−0.5,k_(t)+0.5] to form the point pair setPos_K ₃={{(x _(z1) ,y _(z1)),(x _(y1) ,y _(y1))},{(x _(z2) ,y _(z2)),(x_(y2) ,y _(y2))56 , . . . {(x _(zn) y _(zn)),(x _(yn) ,y _(yn))}},where k_(t)∈k_new.

(5) The binocular visual imaging principle is as shown in FIG. 5, theknown focal length of the binocular visual camera is b=120 mm and theknown focal length of the camera is f(400 mm≤f≤1000 mm), and theparallax d is defined as the position difference of certain pointbetween the corresponding points in two images:d=(x _(zn) −x _(yn))

Where, x_(zn),x_(yn) separately refer to the horizontal coordinates ofthe matching point in the left visual image and right visual image, soas to calculate the space coordinates of certain point P in the leftcamera coordinate system as:

$\left\{ {\begin{matrix}{x^{c} = \frac{b \cdot x_{zn}}{d}} \\{y^{c} = \frac{b \cdot y_{zn}}{d}} \\{z^{c} = \frac{b \cdot f}{d}}\end{matrix}\quad} \right.$

Where, (x^(c),y^(c),z^(c)) is the position information about the currentmatching point in the space coordinate and y_(zn) is the verticalcoordinate of the matching point in the left and right visual images.Calculate the coordinates of all matching points in the left cameracoordinate system according to the formula above, and then calculate thespace coordinate of the point on the power line with the minimumEuclidean distance from the space of the matching point according to theformula above and the obtained horizontal coordinate positions of thepower line (x_(dz1),x_(dz2) . . . x_(dzn)) and (x_(dy1),x_(dy2) . . .x_(dyn)); where the points defined on the 2D coordinate system with thesame vertical coordinate have the minimum Euclidean distance from thespace of the matching point, then directly give the vertical coordinateof the matching point to the corresponding point on the power line toform a corresponding point of the power linePos_D={{(x_(dz1),y_(z1)),(x_(dy1),y_(y1))},{(x_(dz2),y_(z2)),(x_(dy2),y_(y2))},. . . ,{(x_(dzn),y_(zn)),(x_(dyn),y_(yn))}} with the point pair setPos_K₃={{(x_(z1),z_(z1)),(x_(y1),y_(y1))},{(x_(z2),y_(z2)),(x_(y2),y_(y2))},. . . ,{(x_(zn),y_(zn)),(x_(yn),y_(yn))}}, thus to calculate the spacecoordinate (x^(d),y^(d),z^(d)) of certain point D on the power line inthe left camera coordinate system.

(6) Calculate the Euclidean distance J of the matching point from thespace of the power line after the space coordinates of Point P and PointD are obtained, and J is defined asJ=√{square root over ((x ^(c) −x ^(d))²+(y ^(c) −y ^(d))²+(z ^(c) −z^(d))²)}Calculate the Euclidean distance of all matching points in the currentframe of image from the space of the power line in sequence and comparewith the predetermined distance empirical threshold. If J is greaterthan the threshold, complete the annotation of the point in the leftvisual image, complete feedback of the information about the obstaclewhich poses a threat to the power line, back feed the specificinformation about the obstacle on the computer software interface,record the obstacle detection time and geographical location. When thespace vertical distance between the matching point and the power line islower than the given threshold, the current matching point shall not beprocessed and the same treatment shall be applied to all matching pointsin the current frame image. In addition, process the frames of images inthe sequence frame image in all videos in sequence and complete recordof the obstacle annotation and information in the inspection process.

What is claimed is:
 1. A UAV inspection method for power line based onhuman visual system, comprising following steps: Step (1) for UAV videcapture, using UAV armed with binocular visual measuring equipment toobtain video images of distribution of the power line and environmentalinformation on the power line; Step (2) by an image preprocessingmodule, grabbing frames of a sequence of inputted vide images of thepower line, and preprocessing current frame of the image, includingimage graying processing and DoG edge detection, in which, the DoG edgedetection uses DoG results of different parameters to describe the imageedge and applies the same processing to left visual image and rightvisual image in order; Step (3) by a power line detection module,processing the preprocessed image based on mathematical morphology,selecting structural factors in the same direction as the power line toperform repeated dilation and corrosion processing on the image,removing image noise, and using human visual connected domain attentionmechanism to select the largest linear connected domain so as tocomplete segmentation of the power line in the image; respectivelyrecording horizontal coordinate positions (x_(dz1),x_(dz2) . . .x_(dzj)) and (x_(dy1),x_(dy2) . . . x_(dyj)) of the power line in leftvisual image and right visual image, where x_(dz1),x_(dz2) . . . x_(dzj)and x_(dy1),x_(dy2) . . . x_(dyj) refer to the horizonal coordinate of acentral point of a connected domain of j power lines in the left visualimage and right visual image; Step (4) by a binocular image registrationmodule, providing registration for the left visual image and rightvisual image separately, using SURF algorithm to find feature points ofthe left and right visual edge images which have been preprocessed,obtaining descriptor of a current feature point, performing exactmatching for the feature points and recording location information ofthe exact matching point in left and right visual images(x_(z1),y_(z1))˜(x_(y1),y_(y1)), (x_(z2),y_(z2))˜(x_(y2),y_(y2)) . . .(x_(zn),y_(zn))˜(x_(yn),y_(yn)), of which n refers to the number of allfeature points included in single image x_(zn),y_(zn) and x_(yn),y_(yn)refer to position coordinates of corresponding features points in theleft and right visual images, respectively; Step (5) by an obstacledetection and early warning module, based on the horizontal coordinationposition of the power line in left and right visual images(x_(dz1),x_(dz2) . . . x_(dzj)), (x_(dy1),x_(dy2) . . . x_(dyj)) andlocations of the exact matching points in left and right visual images(x_(z1),y_(z1))˜(x_(y1),y_(y1)), (x_(z2),y_(z2))˜(x_(y2),y_(y2)) . . .(x_(zn),y_(zn))˜(x_(yn),y_(yn)), using binocular visual principle tocalculate three-dimensional space coordinates of the matching points andthe power line, and calculating a vertical distance from the matchingpoints to the power line according to the space coordinate information;Step (6) by a result output and feedback module, feeding back obstacleinformation of an obstacle that threatens the power line, when the spacevertical distance between the matching point and the power line ishigher than a predetermined threshold, feeding back detailed informationabout the obstacle to a computer software interface and recordingobstacle detection time and geographical location; when the spacevertical distance between the matching point and the power line is lowerthan the predetermined threshold, not processing the current matchingpoint; applying the same processing to all matching points in thecurrent frame image, feeding back the obstacle information of allobtained frame images in order, and completing record of the obstacleinformation in an inspection process.
 2. The UAV inspection method UAVinspection method for power line based on human visual system accordingto claim 1, wherein, In Step (1), the binocular visual measuringequipment includes two video image capture equipments with the samespecifications and parameters, which are arranged in left and rightvisual forms separately and acquire video image information at a fixedangle of view; when the UAV armed with binocular visual measuringequipment is used for power line inspection, wireless remote control isapplied to the UAV, so that the UAV can have uniform rectilinearmovements on a path in parallel with the power line and above the powerline, and the distribution direction of the power line in the capturevideo image sequence is in parallel with a flight direction of the UAV;the captured power line video image includes left video sequence l_(z)and right video sequence l_(y), and data of the left video sequencel_(z) and the right video sequence l_(y) is stored in a storage deviceinstalled on the UAV, which are transmitted to the image preprocessingmodule of a software system via a mobile wireless network.
 3. The UAVinspection method UAV inspection method for power line based on humanvisual system according to claim 1, wherein, In Step (2), the imagepreprocessing module processes the left video sequence l_(z) and theright video sequence l_(y), which first grabs the frame continuouslyfrom the left video sequence l_(z) and the right video sequence l_(y)separately at a certain interval, which requires that all frame imagescan be pieced together to recover a complete inspection imageinformation and there is no information gap when adjacent frames arepieced together, so as to form the left image sequence T_(z) and rightimage sequence T_(y), of which the left image sequence T_(z) and rightimage sequence T_(y) contain m frames of images, separately areT_(z1),T_(z2) . . . T_(zm) and T_(y1),T_(y2) . . . T_(ym); the imagepreprocessing module preprocesses corresponding frames of left and rightvisual images, and first processes the first frame of left visual imageT_(z1) and right visual image T_(y1), and then processes thecorresponding frame of the left and right visual images in sequenceuntil the end of processing of No.m frame of left visual image T_(zm)and No.m frame of left visual image T_(ym).
 4. The UAV inspection methodUAV inspection method for power line based on human visual systemaccording to claim 3, wherein, preprocessing the first frame of leftvisual image T_(z1) and the first image of right visual image T_(y1)includes image grayscale processing and DoG edge detection, the imagegrayscale processing turns color image into a single-channel grayscaleimage H_(z1) of the first left visual image and a single-channelgrayscale image of the first right visual image H_(y1); in the DoG edgedetection, DoG operation was defined as: $\begin{matrix}{D = {{\frac{1}{2\pi}\left\lbrack {{\frac{1}{\sigma_{1}^{2}}e^{- \frac{({x^{2} + y^{2}})}{2\sigma_{1}^{2}}}} - {\frac{1}{\sigma_{2}^{2}}e^{- \frac{({x^{2} + y^{2}})}{2\sigma_{2}^{2}}}}} \right\rbrack}*H}} \\{= {\left\lbrack {{G\left( {x,y,\sigma_{1}} \right)} - {G\left( {x,y,\sigma_{2}} \right)}} \right\rbrack*H}}\end{matrix}$ where, D refers to an image result after processing, σ₁ isfirst constant while σ₂ is second constant, x and y separately refer tohorizontal and vertical coordinates of the current pixel point in themage, G(x,y,σ₁) and G(x,y,σ₂) separately refer to Gaussian filterfunction of different parameters, H refers to a grayscale image, “*”means performing moving filter toward the whole frame of image, and thepreprocessed images separately are the left visual edge image D_(z1) andthe right visual edge image D_(y1).
 5. The UAV inspection method UAVinspection method for power line based on human visual system accordingto claim 4, wherein, in Step (3), the power line in the left visual edgeimage D_(z1) and the right visual edge image D_(y1) is segmented, B(x)represents structural element, and erosion and dilation operation ofeach point A(x,y) in work space E as defined as:X=E⊗B={A(x,y):B(x)⊂E}  Erosion:Y=E⊕B={A(x,y):B(y)∩E≠Φ}  Dilation: where, ⊂ stands for “included in”, ∩stands for “intersection operation” and Φ stands for null set. If thestructural element B(x) is used to apply erosion to the work space E,the result is the set consisting of all points of the structural elementB(x) included in the work space E includes a structural element B aftertranslation of the structural element B, if the structural element B(x)is used to apply dilation to the work space E, the result is a setconsisting of all non-null set points of an intersection set of thestructural element B and the work space E after translation of thestructural element B.
 6. The UAV inspection method UAV inspection methodfor power line based on human visual system according to claim 5,wherein, a linear structure factor with a length of 3 pixel points andan angle of 90° is selected, the erosion and dilation operation isapplied to the left visual edge image D_(z1) and the right visual edgeimage D_(y1), during which one loop operation includes two erosionoperations and one dilation operation, which lasts for 20 times.
 7. TheUAV inspection method UAV inspection method for power line based onhuman visual system according to claim 5, wherein, for an image whichhas completed the loop of erosion and dilation operation, an area and alength of the connected domain in the image is measured, linear shapeand the area of the connected domain are remained to meet thresholdrequirements, that is the power line, noise in the image is removed andsegmentation of the power line in the image is completed, and thehorizontal coordinate positions of (x_(dz1),x_(dz2) . . . x_(dzj)) and(x_(dz1),x_(dz2) . . . x_(dzj)) of the power line in the left visualedge image D_(z1) and the right visual edge image D_(y1) are separatelyrecorded, where the left visual edge image D_(z1) and the right visualedge image D_(y1) separately contain j power lines and record jhorizontal coordinate positions.
 8. The UAV inspection method for powerline based on human visual system according to claim 7, wherein, in Step(4), SURF algorithm is used to detect the feature points of the leftvisual edge image D_(z1) and the right visual edge image D_(y1), whichcomprises following steps: step (41) using rapid hessian detector toextract the feature points, in which, with regard to certain point inthe left visual edge image D_(z1) and the right visual edge imageD_(y1), when a scale is σ, Hessian matrix at the point is expressed as:${H\left( {x,\sigma} \right)} = \begin{bmatrix}{L_{xx}\left( {x,\sigma} \right)} & {L_{xy}\left( {x,\sigma} \right)} \\{L_{xy}\left( {x,\sigma} \right)} & {L_{yy}\left( {x,\sigma} \right)}\end{bmatrix}$ where, function L_(xx)(x,σ) refers to the second partialderivative of Gaussian function on X-coordinate and a convolution of theleft visual edge image D_(z1) and the right visual edge image D_(y1) atthe point (x,y), which is defined as:${L_{xx}\left( {x,\sigma} \right)} = {D*\frac{\partial^{2}}{\partial x^{2}}{g(\sigma)}}$description of L_(xy)(x,σ) and L_(yy)(x,σ) is separately shown in theformula below:${L_{xy}\left( {x,\sigma} \right)} = {D*\frac{\partial^{2}}{{\partial x}{\partial y}}{g(\sigma)}}$${{L_{yy}\left( {x,\sigma} \right)} = {D*\frac{\partial^{2}}{\partial y^{2}}{g(\sigma)}}},$∂ refers to an operation to obtain partial derivative, where, the scalespace of SURF algorithm is divided by group, images in each group areobtained after convolution of filters of different sizes, and the filtersize will gradually increase; step (42) setting the feature point as acentral point and constructing a box with a size of 20σ along theprincipal direction of the central point, then dividing this region into16 sub-regions, calculating a wavelet response within each sub-region(5σ×5σ) and obtaining 0° direction, vector Σdx, 90° direction and vectorΣdy, 180° direction and vector Σd|x| and 180° direction and vectorΣd|y|, and then constructing a 4D feature vector v=(Σdx,Σ|dx|,Σdy,Σ|dy|)to express each sub-region, and finally forming a 64D descriptor of thepoint; assuming that the size of the left visual edge image D_(z1) andthe right visual edge image D_(y1) are both M×N, and the images areplaced horizontally at the same coordinate axis to form an image withthe size of M×2N, and a set of feature points of the left visual edgeimage D_(z1) and the right visual edge image D_(y1) detected by the SURFmethod is expressed as:Pos1={(x′ ₁ ,y′ ₁),(x′ ₂ ,y′ ₂), . . . ,(x′ _(p) ,y′ _(p))}Pos2={(x ₁ ,y ₁),(x ₂ ,y ₂), . . . ,(x _(q) ,y _(q))}, where, p and qseparately refer to the number of feature points of the left visual edgeimage D_(z1) and the right visual edge image D_(y1); and feature pointmatching method includes the following steps: step a) with regard toeach point i in the feature point set Pos1 of the left visual edgeimage, calculating the Euclidean distance between each point i and allpoints in the feature point set Pos2 of the right visual edge image, andselecting a corresponding point of the minimum Euclidean distance as arough matching point of Point i; step b) calculating the Euclideandistance of all rough matching point pair, sorting the matching pointpairs by the Euclidean distance in ascending order and deleting pointpairs with multiple points towards one point, in which at this time thefeature points in the left visual edge image D_(z1) and the right visualedge image D_(y1) are separately expressed as a feature point set Pos1′of fixed left visual edge image and a feature point set Pos2′ of fixedright visual edge image; step c) selecting the first K₁ pairs ofmatching points in the feature point set Pos1′ of the fixed left visualedge image and the feature point set Pos2′ of the fixed right visualedge image to express asPos_K₁={{(x′₁,y′₁),(x₁,y₁)},{(x′₂,y′₂),(x₂,y₂)}, . . . ,{(x′_(n),y′_(n)),(x_(n),y_(n))}}, which is called Set 1; selecting thefirst K₂ pairs of matching points in the feature point set Pos1′ of thefixed left visual edge image and the feature point set Pos2′ of thefixed right visual edge image to express as Pos_K₂, wherePos_K₂={{(x′₁,y′₁),(x₁,y₁)}, {(x′₂,y′₂),( x₂,y₂)}, . . . , {(x′_(K) ₂,y′_(K) ₂ ),(x_(K) ₂ ,y_(K) ₂ )}}, is called as Set 2, where K₁<K₂; stepd) with regard to all the point pairs in Set 2, calculating a slopebetween two points in a formula below, and rounding it to form a SlopeSet k: k={k₁,k₂, . . . ,k_(w)},${k_{w} = \frac{y_{w}}{x_{w} - x_{w}^{\prime}}},{{1 \leq w \leq K_{2}};}$step e) calculating a frequency of each slope in Slope Set k, screeningthe slope with the frequency of greater than and equal to 2 to form anew set k_new={k₁,k₂, . . . ,k_(t)}, where t refers to the totalquantity of feature points obtained; if the frequency of each slop inSlope Set k is 1, selecting the slope of the first 2K₂/3 pairs of pointsto form a new set k_new; step f) traversing and calculating the slope ofall point pairs in the feature point set Pos1′ of the fixed left visualedge image and the feature point set Pos2′ of the fixed right visualedge image, and screening all the point pairs with the slope fallingbetween the interval of [k_(i)−0.5,k_(i)+0.5] to form the point pair setPos_K₃={{(x_(z1),y_(z1)),(x_(y1),y_(y1))},{(x_(z2),y_(z2)),(x_(y2),y_(y2))},. . . . {(x_(zn),y_(zn)),(x_(yn),y_(yn))}}, where k_(t)∈k_new.
 9. TheUAV inspection method UAV inspection method for power line based onhuman visual system according to claim 8, wherein, In step (41), theSURF algorithm re-specifies an only direction for each interest pointaccording to information about pixel points around the feature points,which comprising following steps: a) taking feature points as center tocalculate the horizontal and vertical responses of Harr wavelet in acircular field with a radius of 6σ, of which a sampling step length is σand a wavelet size is 4σ; b) taking feature points as center to applyGaussian weighting to wavelet response, so that a weight value close tothe feature point is large and a weight value away from the featurepoint is small, and obtaining new horizontal and vertical responses; c)using a fan-shaped window with an angle of 60° to traverse the entirecircle until the total response within a fan-shaped window is thestrongest, wherein at this time a direction within the fan-shaped windowis the principal direction of the interest point.
 10. The UAV inspectionmethod UAV inspection method for power line based on human visual systemaccording to claim 8, wherein, In Step (5), the parallax range betweenbinocular visual camera is b, known focal length of camera is f andparallax d is defined as the position difference of certain pointbetween corresponding points in two images;d=(x _(zn) −x _(yn)) where, x_(zn), x_(yn) separately refer to thehorizontal coordinates of the matching point in the left visual imageand right visual image, so as to calculate the space coordinates ofcertain point P in the left camera coordinate system according toformula: $\left\{ {\begin{matrix}{x^{c} = \frac{b \cdot x_{zn}}{d}} \\{y^{c} = \frac{b \cdot y_{zn}}{d}} \\{z^{c} = \frac{b \cdot f}{d}}\end{matrix}\quad} \right.$ where, (x^(c),y^(c),z^(c)) is positioninformation about the current matching point in the space coordinate andy_(zn) is the vertical coordinate of the matching point in the left andright visual images, the coordinates of all matching points in the leftcamera coordinate system are calculated according to the formula, andthen the space coordinate of the point on the power line with theminimum Euclidean distance from the space of the matching point arecalculated according to the formula and the obtained horizontalcoordinate positions of the power line (x_(dz1),x_(dz2) . . . x_(dzn))and (x_(dy1),x_(dy2) . . . x_(dyn)); where the points defined on the 2Dcoordinate system with the same vertical coordinate have the minimumEuclidean distance from the space of the matching point, then directlygive the vertical coordinate of the matching point to the correspondingpoint on the power line to form a corresponding point of the power linePos_D={{(x_(dz1),y_(z1)),(x_(dy1),y_(y1))},{(x_(dz2),y_(z2)),(x_(dy2),y_(y2))},. . . , {(x_(dzn),y_(zn)),(x_(dyn),y_(yn))}} with the point pair setPos_K₃={{(x_(z1),y_(z1)),(x_(y1),y_(y1))},{(x_(z2),y_(z2)),(x_(y2),y_(y2))},. . . , {(x _(zn) y _(zn)),(x_(yn),y_(yn))}}, thus to calculate thespace coordinate (x^(d),y^(d),z^(d)) of certain point D on the powerline in the left camera coordinate system.
 11. The UAV inspection methodUAV inspection method for power line based on human visual systemaccording to claim 10, wherein, In Step (6), space Euclidean distance Jbetween the matching point and the power line is calculated after thespace coordinates of Point P and Point D are obtained, and J is definedas:J=√{square root over ((x ^(c) −x ^(d))²+(y ^(c) −y ^(d))²+(z ^(c) −z³)²)}.